Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Numeric1
Text1
Categorical9

Alerts

시도코드 has constant value ""Constant
시도명 has constant value ""Constant
기울기 is highly overall correlated with 아이디 and 6 other fieldsHigh correlation
시군구명 is highly overall correlated with 아이디 and 6 other fieldsHigh correlation
20cm 침수심 유발 강우량 is highly overall correlated with 아이디 and 6 other fieldsHigh correlation
10cm 침수심 유발 강우량 is highly overall correlated with 아이디 and 6 other fieldsHigh correlation
시군구코드 is highly overall correlated with 아이디 and 6 other fieldsHigh correlation
y절편 is highly overall correlated with 아이디 and 6 other fieldsHigh correlation
50cm 침수심 유발 강우량 is highly overall correlated with 아이디 and 6 other fieldsHigh correlation
아이디 is highly overall correlated with 시군구코드 and 6 other fieldsHigh correlation
아이디 has unique valuesUnique
격자번호 has unique valuesUnique

Reproduction

Analysis started2024-04-17 14:40:57.153031
Analysis finished2024-04-17 14:40:57.901525
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30847.82
Minimum22552
Maximum31651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T23:40:57.981359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22552
5-th percentile23513
Q131336.75
median31446.5
Q331549.25
95-th percentile31646.05
Maximum31651
Range9099
Interquartile range (IQR)212.5

Descriptive statistics

Standard deviation2197.5398
Coefficient of variation (CV)0.07123809
Kurtosis9.9367437
Mean30847.82
Median Absolute Deviation (MAD)106.5
Skewness-3.4086981
Sum3084782
Variance4829181.1
MonotonicityStrictly increasing
2024-04-17T23:40:58.137869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22552 1
 
1.0%
31539 1
 
1.0%
31549 1
 
1.0%
31548 1
 
1.0%
31547 1
 
1.0%
31546 1
 
1.0%
31545 1
 
1.0%
31544 1
 
1.0%
31543 1
 
1.0%
31542 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
22552 1
1.0%
22553 1
1.0%
22652 1
1.0%
22653 1
1.0%
22753 1
1.0%
23553 1
1.0%
23653 1
1.0%
31041 1
1.0%
31042 1
1.0%
31043 1
1.0%
ValueCountFrequency (%)
31651 1
1.0%
31650 1
1.0%
31649 1
1.0%
31648 1
1.0%
31647 1
1.0%
31646 1
1.0%
31645 1
1.0%
31644 1
1.0%
31643 1
1.0%
31642 1
1.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T23:40:58.422526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row다마8498
2nd row다마8499
3rd row다마8598
4th row다마8599
5th row다마8699
ValueCountFrequency (%)
다마8498 1
 
1.0%
다바8205 1
 
1.0%
다바8216 1
 
1.0%
다바8215 1
 
1.0%
다바8214 1
 
1.0%
다바8213 1
 
1.0%
다바8212 1
 
1.0%
다바8211 1
 
1.0%
다바8210 1
 
1.0%
다바8209 1
 
1.0%
Other values (90) 90
90.0%
2024-04-17T23:40:59.151896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
다 100
16.7%
8 99
16.5%
바 93
15.5%
1 79
13.2%
0 61
10.2%
2 41
6.8%
9 31
 
5.2%
3 31
 
5.2%
7 28
 
4.7%
4 11
 
1.8%
Other values (3) 26
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 99
24.8%
1 79
19.8%
0 61
15.2%
2 41
10.2%
9 31
 
7.8%
3 31
 
7.8%
7 28
 
7.0%
4 11
 
2.8%
5 11
 
2.8%
6 8
 
2.0%
Other Letter
ValueCountFrequency (%)
다 100
50.0%
바 93
46.5%
마 7
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
8 99
24.8%
1 79
19.8%
0 61
15.2%
2 41
10.2%
9 31
 
7.8%
3 31
 
7.8%
7 28
 
7.0%
4 11
 
2.8%
5 11
 
2.8%
6 8
 
2.0%
Hangul
ValueCountFrequency (%)
다 100
50.0%
바 93
46.5%
마 7
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
다 100
50.0%
바 93
46.5%
마 7
 
3.5%
ASCII
ValueCountFrequency (%)
8 99
24.8%
1 79
19.8%
0 61
15.2%
2 41
10.2%
9 31
 
7.8%
3 31
 
7.8%
7 28
 
7.0%
4 11
 
2.8%
5 11
 
2.8%
6 8
 
2.0%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
30
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 100
100.0%

Length

2024-04-17T23:40:59.284067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:40:59.378676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 100
100.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
대전
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전
2nd row대전
3rd row대전
4th row대전
5th row대전

Common Values

ValueCountFrequency (%)
대전 100
100.0%

Length

2024-04-17T23:40:59.470954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:40:59.554966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전 100
100.0%

시군구코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
30200
70 
30170
28 
30110
 
2

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30170
2nd row30170
3rd row30170
4th row30170
5th row30170

Common Values

ValueCountFrequency (%)
30200 70
70.0%
30170 28
 
28.0%
30110 2
 
2.0%

Length

2024-04-17T23:40:59.641921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:40:59.729965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30200 70
70.0%
30170 28
 
28.0%
30110 2
 
2.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
유성구
70 
서구
28 
동구
 
2

Length

Max length3
Median length3
Mean length2.7
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서구
2nd row서구
3rd row서구
4th row서구
5th row서구

Common Values

ValueCountFrequency (%)
유성구 70
70.0%
서구 28
 
28.0%
동구 2
 
2.0%

Length

2024-04-17T23:40:59.843638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:40:59.942479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 70
70.0%
서구 28
 
28.0%
동구 2
 
2.0%

기울기
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.98
51 
8.57
33 
6.95
13 
1.48
 
2
2.01
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0.98
2nd row0.98
3rd row8.57
4th row8.57
5th row8.57

Common Values

ValueCountFrequency (%)
0.98 51
51.0%
8.57 33
33.0%
6.95 13
 
13.0%
1.48 2
 
2.0%
2.01 1
 
1.0%

Length

2024-04-17T23:41:00.045456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:41:00.150153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.98 51
51.0%
8.57 33
33.0%
6.95 13
 
13.0%
1.48 2
 
2.0%
2.01 1
 
1.0%

y절편
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-4.39
51 
-35.13
33 
-29.35
13 
-6.24
 
2
-8.75
 
1

Length

Max length6
Median length5
Mean length5.46
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row-4.39
2nd row-4.39
3rd row-35.13
4th row-35.13
5th row-35.13

Common Values

ValueCountFrequency (%)
-4.39 51
51.0%
-35.13 33
33.0%
-29.35 13
 
13.0%
-6.24 2
 
2.0%
-8.75 1
 
1.0%

Length

2024-04-17T23:41:00.272275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:41:00.376545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.39 51
51.0%
35.13 33
33.0%
29.35 13
 
13.0%
6.24 2
 
2.0%
8.75 1
 
1.0%

10cm 침수심 유발 강우량
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
5.42
51 
50.61
33 
40.13
13 
8.59
 
2
11.4
 
1

Length

Max length5
Median length4
Mean length4.46
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row5.42
2nd row5.42
3rd row50.61
4th row50.61
5th row50.61

Common Values

ValueCountFrequency (%)
5.42 51
51.0%
50.61 33
33.0%
40.13 13
 
13.0%
8.59 2
 
2.0%
11.4 1
 
1.0%

Length

2024-04-17T23:41:00.504122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:41:00.600922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5.42 51
51.0%
50.61 33
33.0%
40.13 13
 
13.0%
8.59 2
 
2.0%
11.4 1
 
1.0%

20cm 침수심 유발 강우량
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
15.22
51 
136.36
33 
109.61
13 
23.42
 
2
31.54
 
1

Length

Max length6
Median length5
Mean length5.46
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row15.22
2nd row15.22
3rd row136.36
4th row136.36
5th row136.36

Common Values

ValueCountFrequency (%)
15.22 51
51.0%
136.36 33
33.0%
109.61 13
 
13.0%
23.42 2
 
2.0%
31.54 1
 
1.0%

Length

2024-04-17T23:41:00.708622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:41:00.811489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15.22 51
51.0%
136.36 33
33.0%
109.61 13
 
13.0%
23.42 2
 
2.0%
31.54 1
 
1.0%

50cm 침수심 유발 강우량
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
44.64
51 
393.59
33 
318.05
13 
67.9
 
2
91.98
 
1

Length

Max length6
Median length5
Mean length5.44
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row44.64
2nd row44.64
3rd row393.59
4th row393.59
5th row393.59

Common Values

ValueCountFrequency (%)
44.64 51
51.0%
393.59 33
33.0%
318.05 13
 
13.0%
67.9 2
 
2.0%
91.98 1
 
1.0%

Length

2024-04-17T23:41:00.930438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:41:01.030263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44.64 51
51.0%
393.59 33
33.0%
318.05 13
 
13.0%
67.9 2
 
2.0%
91.98 1
 
1.0%

Interactions

2024-04-17T23:40:57.576190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T23:41:01.104240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디격자번호시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0001.0000.9550.9550.7160.7160.7160.7160.716
격자번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구코드0.9551.0001.0001.0000.7660.7660.7660.7660.766
시군구명0.9551.0001.0001.0000.7660.7660.7660.7660.766
기울기0.7161.0000.7660.7661.0001.0001.0001.0001.000
y절편0.7161.0000.7660.7661.0001.0001.0001.0001.000
10cm 침수심 유발 강우량0.7161.0000.7660.7661.0001.0001.0001.0001.000
20cm 침수심 유발 강우량0.7161.0000.7660.7661.0001.0001.0001.0001.000
50cm 침수심 유발 강우량0.7161.0000.7660.7661.0001.0001.0001.0001.000
2024-04-17T23:41:01.211753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기울기시군구명20cm 침수심 유발 강우량10cm 침수심 유발 강우량시군구코드y절편50cm 침수심 유발 강우량
기울기1.0000.7651.0001.0000.7651.0001.000
시군구명0.7651.0000.7650.7651.0000.7650.765
20cm 침수심 유발 강우량1.0000.7651.0001.0000.7651.0001.000
10cm 침수심 유발 강우량1.0000.7651.0001.0000.7651.0001.000
시군구코드0.7651.0000.7650.7651.0000.7650.765
y절편1.0000.7651.0001.0000.7651.0001.000
50cm 침수심 유발 강우량1.0000.7651.0001.0000.7651.0001.000
2024-04-17T23:41:01.312781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0000.7470.7470.6920.6920.6920.6920.692
시군구코드0.7471.0001.0000.7650.7650.7650.7650.765
시군구명0.7471.0001.0000.7650.7650.7650.7650.765
기울기0.6920.7650.7651.0001.0001.0001.0001.000
y절편0.6920.7650.7651.0001.0001.0001.0001.000
10cm 침수심 유발 강우량0.6920.7650.7651.0001.0001.0001.0001.000
20cm 침수심 유발 강우량0.6920.7650.7651.0001.0001.0001.0001.000
50cm 침수심 유발 강우량0.6920.7650.7651.0001.0001.0001.0001.000

Missing values

2024-04-17T23:40:57.674474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T23:40:57.843463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
022552다마849830대전30170서구0.98-4.395.4215.2244.64
122553다마849930대전30170서구0.98-4.395.4215.2244.64
222652다마859830대전30170서구8.57-35.1350.61136.36393.59
322653다마859930대전30170서구8.57-35.1350.61136.36393.59
422753다마869930대전30170서구8.57-35.1350.61136.36393.59
523553다마949930대전30110동구1.48-6.248.5923.4267.9
623653다마959930대전30110동구1.48-6.248.5923.4267.9
731041다바770930대전30200유성구0.98-4.395.4215.2244.64
831042다바771030대전30200유성구0.98-4.395.4215.2244.64
931043다바771130대전30200유성구0.98-4.395.4215.2244.64
아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
9031642다바831030대전30200유성구8.57-35.1350.61136.36393.59
9131643다바831130대전30200유성구8.57-35.1350.61136.36393.59
9231644다바831230대전30200유성구8.57-35.1350.61136.36393.59
9331645다바831330대전30200유성구8.57-35.1350.61136.36393.59
9431646다바831430대전30200유성구8.57-35.1350.61136.36393.59
9531647다바831530대전30200유성구8.57-35.1350.61136.36393.59
9631648다바831630대전30200유성구8.57-35.1350.61136.36393.59
9731649다바831730대전30200유성구8.57-35.1350.61136.36393.59
9831650다바831830대전30200유성구6.95-29.3540.13109.61318.05
9931651다바831930대전30200유성구6.95-29.3540.13109.61318.05